Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "142" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 23 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 21 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459838 | digital_ok | 100.00% | 26.98% | 100.00% | 0.00% | 100.00% | 0.00% | 35.353544 | 23.900321 | 1.073636 | 23.864935 | 16.480348 | 29.612171 | 0.986164 | 1.539855 | 0.4388 | 0.0469 | 0.2505 | 5.820874 | 1.367242 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0656 | 0.0664 | 0.0029 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.510908 | 2.274409 | 1.447380 | 2.777908 | 1.806757 | -0.113859 | 1.484343 | -0.598383 | 0.0612 | 0.0670 | 0.0021 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 3.384124 | 4.613968 | 0.002964 | 3.518470 | 12.619948 | 3.217124 | 4.835138 | 7.166019 | 0.0542 | 0.0454 | 0.0006 | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 37.453153 | 41.349913 | 0.514366 | 26.218012 | 14.706527 | 11.594013 | 4.059624 | 1.908979 | 0.4593 | 0.0399 | 0.2929 | 8.905931 | 1.328337 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.460389 | 2.631880 | 0.076194 | 0.203294 | 0.611454 | -0.489971 | 5.409943 | 3.537760 | 0.0765 | 0.0812 | 0.0068 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 36.240516 | 40.126493 | 1.112724 | 37.263956 | 32.815144 | 35.973463 | 5.088813 | 5.021914 | 0.4744 | 0.0428 | 0.2507 | 11.067112 | 1.354312 |
| 2459829 | digital_ok | 100.00% | 30.08% | 100.00% | 0.00% | 100.00% | 0.00% | 51.675096 | 40.359856 | 1.361335 | 30.437867 | 15.343505 | 31.843730 | 5.770636 | 6.520201 | 0.4411 | 0.0448 | 0.2341 | -0.000000 | -0.000000 |
| 2459828 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 29.569770 | 33.256778 | 1.283136 | 32.544275 | 31.937624 | 33.247138 | 9.586605 | 10.665924 | 0.4835 | 0.0446 | 0.2688 | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 100.00% | 27.39% | 100.00% | 0.00% | 100.00% | 0.00% | 39.487431 | 30.928520 | 2.029461 | 37.098140 | 13.470020 | 26.036777 | 1.514940 | 0.513113 | 0.4471 | 0.0420 | 0.2136 | 2.166178 | 0.887705 |
| 2459826 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.191429 | 30.512535 | 1.889163 | 40.940310 | 41.652206 | 45.090219 | 5.549476 | 6.834801 | 0.5041 | 0.0437 | 0.2606 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 29.851257 | 32.683078 | 1.885854 | 32.714852 | 25.903865 | 25.608677 | 0.199815 | 0.249606 | 0.5062 | 0.0406 | 0.2249 | -0.000000 | -0.000000 |
| 2459824 | digital_ok | 100.00% | 36.07% | 100.00% | 0.00% | 100.00% | 0.00% | 35.894237 | 23.392589 | 1.625610 | 26.586605 | 11.072148 | 19.024303 | 1.471240 | 2.081262 | 0.4392 | 0.0412 | 0.1799 | 0.000000 | 0.000000 |
| 2459823 | digital_ok | 100.00% | 34.12% | 100.00% | 0.00% | 100.00% | 0.00% | 21.604792 | 28.222044 | 6.587560 | 48.396396 | 37.127597 | 36.133838 | 26.995375 | 30.120324 | 0.4440 | 0.0411 | 0.1932 | 0.000000 | 0.000000 |
| 2459822 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.406267 | 30.592361 | 3.236734 | 44.529283 | 24.573683 | 29.531329 | 1.360510 | 0.587900 | 0.5158 | 0.0435 | 0.2307 | 17.511482 | 1.411585 |
| 2459821 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 29.369593 | 34.592146 | 3.500386 | 45.212136 | 22.077955 | 26.033175 | 1.178546 | -0.035280 | 0.5068 | 0.0424 | 0.2694 | 2.346272 | 1.086743 |
| 2459820 | digital_ok | 100.00% | 32.76% | 100.00% | 0.00% | 100.00% | 0.00% | 37.207637 | 30.468985 | 1.771834 | 37.058869 | 37.921950 | 68.783597 | 5.085474 | 4.520039 | 0.4562 | 0.0456 | 0.2526 | 11.512274 | 1.491013 |
| 2459817 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.945766 | 28.993814 | 5.038012 | 44.001442 | 33.161985 | 36.638513 | 1.147861 | 1.315495 | 0.5122 | 0.0451 | 0.2957 | 9.218119 | 1.285980 |
| 2459816 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 22.756219 | 24.543665 | 2.155470 | 44.808990 | 39.767981 | 45.369020 | 4.544483 | 7.462963 | 0.5122 | 0.0448 | 0.3360 | 8.700276 | 1.296904 |
| 2459815 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 22.157926 | 26.482324 | 5.151574 | 48.312660 | 45.356013 | 48.191162 | 8.957006 | 10.893870 | 0.5110 | 0.0426 | 0.3259 | 13.768105 | 1.390612 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Shape | 35.353544 | 23.900321 | 35.353544 | 23.864935 | 1.073636 | 29.612171 | 16.480348 | 1.539855 | 0.986164 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 2.777908 | 2.274409 | 0.510908 | 2.777908 | 1.447380 | -0.113859 | 1.806757 | -0.598383 | 1.484343 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Temporal Variability | 12.619948 | 4.613968 | 3.384124 | 3.518470 | 0.002964 | 3.217124 | 12.619948 | 7.166019 | 4.835138 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Shape | 41.349913 | 37.453153 | 41.349913 | 0.514366 | 26.218012 | 14.706527 | 11.594013 | 4.059624 | 1.908979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Temporal Discontinuties | 5.409943 | 0.460389 | 2.631880 | 0.076194 | 0.203294 | 0.611454 | -0.489971 | 5.409943 | 3.537760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Shape | 40.126493 | 36.240516 | 40.126493 | 1.112724 | 37.263956 | 32.815144 | 35.973463 | 5.088813 | 5.021914 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Shape | 51.675096 | 40.359856 | 51.675096 | 30.437867 | 1.361335 | 31.843730 | 15.343505 | 6.520201 | 5.770636 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Shape | 33.256778 | 33.256778 | 29.569770 | 32.544275 | 1.283136 | 33.247138 | 31.937624 | 10.665924 | 9.586605 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Shape | 39.487431 | 39.487431 | 30.928520 | 2.029461 | 37.098140 | 13.470020 | 26.036777 | 1.514940 | 0.513113 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Temporal Variability | 45.090219 | 30.512535 | 26.191429 | 40.940310 | 1.889163 | 45.090219 | 41.652206 | 6.834801 | 5.549476 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 32.714852 | 32.683078 | 29.851257 | 32.714852 | 1.885854 | 25.608677 | 25.903865 | 0.249606 | 0.199815 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | ee Shape | 35.894237 | 35.894237 | 23.392589 | 1.625610 | 26.586605 | 11.072148 | 19.024303 | 1.471240 | 2.081262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 48.396396 | 28.222044 | 21.604792 | 48.396396 | 6.587560 | 36.133838 | 37.127597 | 30.120324 | 26.995375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 44.529283 | 26.406267 | 30.592361 | 3.236734 | 44.529283 | 24.573683 | 29.531329 | 1.360510 | 0.587900 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 45.212136 | 34.592146 | 29.369593 | 45.212136 | 3.500386 | 26.033175 | 22.077955 | -0.035280 | 1.178546 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Temporal Variability | 68.783597 | 37.207637 | 30.468985 | 1.771834 | 37.058869 | 37.921950 | 68.783597 | 5.085474 | 4.520039 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 44.001442 | 23.945766 | 28.993814 | 5.038012 | 44.001442 | 33.161985 | 36.638513 | 1.147861 | 1.315495 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Temporal Variability | 45.369020 | 24.543665 | 22.756219 | 44.808990 | 2.155470 | 45.369020 | 39.767981 | 7.462963 | 4.544483 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Power | 48.312660 | 26.482324 | 22.157926 | 48.312660 | 5.151574 | 48.191162 | 45.356013 | 10.893870 | 8.957006 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |